Novel method for coke optical texture measurement and application on coke derived from different ranks of coking coal

Abstract

The optical texture of coke is undoubtedly one of the important factors that determined many properties of coke, but it still mostly depends on the skills and experience of manual analysis. In this paper, a novel automatic measurement method is proposed for coke optical texture. this method through two analyzer angle images(10° and 170°) under polarizing microscope to recognize isochromatic regions which occurs color changed between blue and red by image processing, then characterize the dimensional sizes of isochromatic regions and classify them into different anisotropic texture type. Two fitted ellipse methods are used to characterize the regions' dimensional sizes by the range of shape factor S, followed by classifying different types of anisotropic texture according to the long and short axes of fitted ellipse. An automatic image acquisition and analysis system is established based on the above method. Finally, eight coke samples derived from different ranks of coking coal were selected to test the proposed method. The result shows that it can effectively recognize different types of anisotropic texture and provide composition of different anisotropic texture type for each coke. Moreover, the anisotropic degree of coke shows a significant positive correlation with parent coal rank and a negative correlation with coke reactivity index (CRI).

A Comprehensive Review of Characterization Methods for Metallurgical Coke Structures

“…This is the principle to distinguish these two kinds of optical textures. Sun et al [ 97 ] developed an automatic measurement method for the visual texture of coke under the polarizing microscope. Figure 17 shows the optical images of the same field area of the coke from different analyzer angles.…”

Quantitative Characterization of Coke’s Optical Texture Enables Control of Coke Morphology

“…The computer-automated nature of this method allows for large numbers of images to be analyzed with ease, extracting vast quantities of data from each individual image, enabling reliable statistical analysis, all while avoiding operator error or bias. We use this method to compute an optical texture index (OTI), following a similar procedure previously developed for needle coke − to create a shot index to rank the propensity of a feed and process condition to forming shot coke. In summary, by quantitatively characterizing the sample’s optical texture, the so-called OTI, we are able to numerically rank the degree to which the material exhibits a sponge or shot morphology.…”